7 papers
HDCNet: A Hybrid Depth Completion Network for Grasping Transparent and Reflective Objects
Guanghu Xie, Mingxu Li, Songwei Wu +4
Depth perception of transparent and reflective objects has long been a critical challenge in robotic manipulation.Conventional depth sensors often fail to provide reliable measurem…
DCIRNet: Depth Completion with Iterative Refinement for Dexterous Grasping of Transparent and Reflective Objects
Guanghu Xie, Zhiduo Jiang, Yonglong Zhang +4
Transparent and reflective objects in everyday environments pose significant challenges for depth sensors due to their unique visual properties, such as specular reflections and li…
Diffusion as Reasoning: Enhancing Object Navigation via Diffusion Model Conditioned on LLM-based Object-Room Knowledge
Yiming Ji, Kaijie Yun, Yang Liu +4
The Object Navigation (ObjectNav) task aims to guide an agent to locate target objects in unseen environments using partial observations. Prior approaches have employed location pr…
HTMNet: A Hybrid Network with Transformer-Mamba Bottleneck Multimodal Fusion for Transparent and Reflective Objects Depth Completion
Guanghu Xie, Yonglong Zhang, Zhiduo Jiang +4
Transparent and reflective objects pose significant challenges for depth sensors, resulting in incomplete depth information that adversely affects downstream robotic perception and…
Learning Perceptive Humanoid Locomotion over Challenging Terrain
Wandong Sun, Baoshi Cao, Long Chen +4
Humanoid robots are engineered to navigate terrains akin to those encountered by humans, which necessitates human-like locomotion and perceptual abilities. Currently, the most reli…
Neural-Network-Driven Reward Prediction as a Heuristic: Advancing Q-Learning for Mobile Robot Path Planning
Yiming Ji, Kaijie Yun, Yang Liu +2
Q-learning is a widely used reinforcement learning technique for solving path planning problems. It primarily involves the interaction between an agent and its environment, enablin…